Resilience-driven Planning of Electric Power Systems Against Extreme Weather Events

With the increasing frequency of natural disasters, operators must prioritize improvements in the existing electric power grid infrastructure to enhance the resilience of the grid. Resilience to extreme weather events necessitates lowering the impacts of high-impact, low-probability (HILP) events, which is only possible when such events are considered during the planning stage. This paper proposes a two-stage stochastic planning model where the generation dispatch, line hardening, line capacity expansion, and distributed generation sizing and siting decisions are proactively decided to minimize the overall load shed and its risk for extreme weather scenarios, where the risk is modeled using conditional value-at-risk. To alleviate computational complexity without sacrificing solution quality, a representative scenario sampling method is used. Finally, the overall framework is tested on a standard IEEE reliability test system to evaluate the effectiveness of the proposed approach. Several planning portfolios are presented that can help system planners identify trade-offs between system resilience, planning budget, and risk aversion.